FIFA 19 Data Exploration¶
Importing Libraries ¶
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import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
# loading dataset
df = pd.read_excel('data.xlsx')
df.head()
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we wouldn't be using all of the features present here in our data set for our analysis, so we pick only those needed.
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df_copy = df.copy()
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df = df[['Age','Nationality', 'Club','Release Clause','Wage','Value','Preferred Foot','Position','Weight','Finishing',
'Dribbling','BallControl','Stamina','Jumping','SlidingTackle','GKReflexes','Body Type']]
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df
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df.info()
Data cleaning¶
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# checjing for missing values
df.isnull().sum()
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# removing rows with missing values from our dataset
df.dropna(inplace=True)
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df.isnull().sum()
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# remove lbs in weight column
df['Weight'] = df['Weight'].str.replace('lbs','')
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# change weight column from object to int
df['Weight'] = df['Weight'].astype(int)
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# checking if the changes has been implemented
df['Weight']
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From observation, value, wage and release clause column represents thounsand with 'K' and million with 'M'. We can replace k with '000' and M with '000000' and also remove point(.) and change their data types to int.
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# removing decimal point
df['Value'] = df['Value'].str.replace('.','')
df['Release Clause'] = df['Release Clause'].str.replace('.','')
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# replace K with '000' and M with '000000'
df['Value'] = df['Value'].str.replace('K','000')
df['Value'] = df['Value'].str.replace('M', '00000')
df['Release Clause'] = df['Release Clause'].str.replace('K','000')
df['Release Clause'] = df['Release Clause'].str.replace('M', '00000')
df['Wage'] = df['Wage'].str.replace('K','000')
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df['Value']
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# remove the currency symbol '€' from the dataset
df['Value'] = df['Value'].str.replace('€','')
df['Release Clause'] = df['Release Clause'].str.replace('€','')
df['Wage'] = df['Wage'].str.replace('€','')
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df.head(3)
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# change data types
df['Value'] = df['Value'].astype(int)
df['Wage'] = df['Wage'].astype(int)
df['Release Clause'] = df['Release Clause'].astype(int)
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df.describe(include = 'all')
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df.to_csv('fifa_19_cleaned.csv', index=False)
Observation¶
- The minimum age of a player is 16yrs and 75% of players are below 29 yrs of age.
- The maximum weight of a player is 243lbs.
- 50% of players have stamina rating of 66.
Univariate Exploration ¶
What is the Age distribution of players?¶
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df['Age'].hist();
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.title('Age distribution of players');
- We have a right skewed distribution and majority of players are in their 20's.
What is the weight distribution of players?¶
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sns.distplot(df['Weight']);
plt.ylabel('Frequency')
plt.title('Weight distributions of players');
- We can see that the weight distribution of players looks normal.
What club is the top 10 most populated by players?¶
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def n_bar_plot(dataFrame, col, a, b, title, x_label, y_label ):
dataFrame[col].value_counts()[a:b].plot(kind='bar');
plt.title(title);
plt.xlabel(x_label)
plt.ylabel(y_label);
n_bar_plot(df, 'Club', 0, 10, 'Most popular clubs', 'Club', 'count' )
- We can see that 6 out of 10 clubs are from England, with 3 spanish clubs and 1 French.
with the findings above, lets take a look at players nationality
Top 10 most player Nationality?¶
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n_bar_plot(df, 'Nationality', 0, 10, 'Most Players Nationality', 'Nationality', 'Frequency')
- Most players as we can see are from England which could also play a role to why the most populated clubs are England clubs.
Lets look at most preferred foot by players¶
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def bar_plot(dataFrame, col, title, x_label, y_label):
dataFrame[col].value_counts().plot(kind='bar');
plt.title(title)
plt.xlabel(x_label)
plt.ylabel(y_label);
bar_plot(df, 'Preferred Foot', 'Players Most Preferred Foot', 'Preferred Foot', 'count')
- As we can see, most of the players are right footed and this is also true for the real world.
Players positioning¶
We have gotten insight on players preferred foot, lets take a look at players position to see where most players are situated on the pitch
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plt.figure(figsize=(8,4))
bar_plot(df, 'Position', 'Players positioning', 'Position', 'count')
- Most of the players in FIFA19 are strikers (ST) followed by GoalKeepers(GK)
Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?¶
- I was able to notice that most players on the fifa 19 dataset are in their 20's.
- The weights of players are normal
- Though it didn't come as a suprise but most of the players are right footed.
- I also went on to look at top 10 clubs and i found 6 England clubs from the Premeire League as part of it which shows the relevance of the Premiere League in the space of football.
- Further looking at the dataset, i found out that the dataset is populated mostly by strikers (ST) according to their positions/role on the pitch.
Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?¶
Before making my visualizations, There were some missing observations which i cleaned in order to avoid errors and be able to make good analysis. Also, some of the column data types were changed to help get a perfect data for the job.
Bivariate Exploration ¶
lets look at the correlation between Weight and Finishing
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df.plot(x='Weight', y='Finishing', kind='scatter', title='Weight by finishing');
- There seems to be no correlation between a players weight and fininishing which is right, as the weight of a player do not necessarily mean he will deliver a good finishing.
Correlation between Ball_control and finishing¶
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df.plot(y='BallControl', x='Finishing', kind='scatter', title='finishing and Ball control');
- there's a positive correlation between Ballcontrol and Finishing and to be honest it ought to. For a player to deliver good finishing, then he has to be able to control the ball pretty well.
Correlation between Dribbling and Finishing¶
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df.plot(y='Dribbling', x='Finishing', kind='scatter', title='correlation between Dribbling and Finishing');
- As we can see theres a strong positive correlation here with which we can all agree to.
Lets look at the value of player based on body type
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base_color = sns.color_palette()[0]
sns.violinplot(x='Body Type', y='Value', color = base_color, data=df);
plt.title('Value of player by Body type');
- As we can see, players with normal body type are valued more compared to the rest body type.
Club with most player wage¶
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club_by_wage =df.groupby('Club')['Wage'].sum().sort_values(ascending=False).head(10)
club_by_wage.plot.bar(title='Top 10 club with most player wage')
plt.ylabel('Amount');
- Real Madrid a spanish club is the club with most wage.
Most preferred foot of players¶
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sns.catplot(x='Preferred Foot',y='Value', kind='bar', data=df);
plt.title('Most Preferred foot of players by Value');
- Left footed players as we have seen have more value when comapared to right footed players.
Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?¶
- I took a look on the correlation between weight and finishing anf of course there was a negative correlation as truly, weight of a player do not necessarily affects its finishing.
- From there i decided to see if ball control could affect a players finishing and the result provided a high positive correlation of which truly, for a player to have a good finishing then he must be able to control the ball to a certain point.
- I went on further to investigate dribbling and finishing and like the last observation, the result was also a strong positive correlation.
- I also decide to check a players value based on his preferred foot and i was suprise to find out that most left footed players are valued more.
Multivariate Exploration¶
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sns.pairplot(df);
Age, Weight and Body_type of players¶
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g = sns.FacetGrid(data = df, hue = 'Body Type', height = 8, aspect = 1.5, palette = 'viridis_r')
g.map(plt.scatter, 'Weight', 'Age');
g.add_legend();
plt.title('Age vs Weight and Body_type of players');
- from observation, most young players weigh less and in accordance to their body type, they are lean
Players Body_type, Value and Preferred foot¶
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sns.catplot(x='Body Type',y='Value', hue='Preferred Foot', kind='bar', data=df);
plt.title('Body_type vs Value and Preferred_foot');
- players of stocky bosy type are more left footed and they have more value
Talk about some of the relationships you observed in this part of the investigation. Were there features that strengthened each other in terms of looking at your feature(s) of interest?¶
- There's a strong corelation between a players stamina and how he controls the ball. Which tells us now that players with good staminas can/are more like to control the ball more better and this feature can be used in sourcing a player into a club.
- Most of the young players have lean body type and they weigh less.
Were there any interesting or surprising interactions between features?¶
- it was interesting to find out that Goal keeper reflex has low positive correlation with his age.
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